Elizabethkingia (oligotyping)

Load packages, paths, functions

# Load main packages, paths and custom functions
source("../../../source/main_packages.R")
source("../../../source/paths.R")
source("../../../source/functions.R")

# Load supplementary packages
packages <- c("RColorBrewer", "ggpubr", "cowplot", "Biostrings", "openxlsx", "kableExtra")
invisible(lapply(packages, require, character.only = TRUE))

Preparation

Tables preparation

Seqtab

# move to oligotyping directory
setwd(paste0(path_oligo,"/elizabethkingia/oligotyping_Elizabethkingia_sequences-c1-s1-a0.0-A0-M10"))

# load the matrix count table
matrix_count <- read.table("MATRIX-COUNT.txt", header = TRUE) %>% t()

# arrange it
colnames(matrix_count) <- matrix_count[1,]
matrix_count <- matrix_count[-1,]
matrix_count <- matrix_count %>% as.data.frame()

# print it
matrix_count %>%
  kbl() %>%
  kable_paper("hover", full_width = F)
CTC1 CTC10 CTC11 CTC12 CTC13 CTC14 CTC15 CTC2 CTC3 CTC4 CTC5 CTC6 CTC7 CTC9 NP27 NP30 NP36 S126 S146 S147 S154 S160 S163 S164 S165 S166 S18 S19 S20 S21 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S44 S45 S46 S47 S48 S49 S50 S51 S52
C 19626 592 5363 287 2687 1000 14770 1700 2520 1075 326 10704 15 2688 11 1 6 1 4 1 1 1 1 6 2 2 1 12104 12 6 33 53 3 8 1755 5 4 1933 4229 2020 2060 225 392 17944 3328 4744 4931 4388 1 36 32 9 6 221 43 71 61 47
G 3017 80 963 43 437 150 2088 254 378 165 60 1649 5 399 1 0 1 0 0 0 0 0 0 1 0 4 0 277 0 0 5 1 0 1 27 0 1 55 121 36 66 5 10 390 71 73 110 130 0 1 1 0 1 6 2 3 1 3

Taxonomy

# move to oligotyping directory
setwd(paste0(path_oligo,"/elizabethkingia/oligotyping_Elizabethkingia_sequences-c1-s1-a0.0-A0-M10"))

# load the fasta table
fasta <- readDNAStringSet("OLIGO-REPRESENTATIVES.fasta")

# arrange it
fasta <- fasta %>% as.data.frame()
colnames(fasta) <- "seq"
fasta$oligotype <- rownames(fasta)
fasta <- fasta %>% dplyr::select(-c(seq))

# print it
fasta %>%
  kbl() %>%
  kable_paper("hover", full_width = F)
oligotype
C C
G G

Change oligotype name by oligotype / MED nodes in the matrix count

# Reference file 

## move to tsv directory
setwd(path_tsv)

## load the reference table
ref_oligo_med2 <- read.table("2B_REF_info_elizabethkingia.tsv", sep="\t", header = TRUE)

## select only the 2 oligotypes of Elizabethkingia
ref_oligo_med2 <- ref_oligo_med2[!is.na(ref_oligo_med2$oligotype),]

## change order of columns
ref_oligo_med2 <- ref_oligo_med2 %>% select(c(seq, oligotype, MED_node_frequency_size, OLIGO_oligotype_frequency_size))

## create a column with reference name (will be used in plots)
ref_oligo_med2$ref <- paste0("oligotype_", ref_oligo_med2$OLIGO_oligotype_frequency_size, " / node_", ref_oligo_med2$MED_node_frequency_size)

## create a copy of fasta 
fasta2 <- fasta

# Matrix count

## create an oligotype column in the matrix count
matrix_count$oligotype <- rownames(matrix_count)

## change order of columns
matrix_count <- matrix_count %>% dplyr::select(c(oligotype, everything()))

## merge the matrix count and the reference dataframe
matrix_count2 <- matrix_count %>% merge(ref_oligo_med2 %>% dplyr::select(-c(seq)), by="oligotype")

## change order of columns
matrix_count2 <- matrix_count2 %>% dplyr::select(c(oligotype, MED_node_frequency_size, OLIGO_oligotype_frequency_size, ref, everything()))

## change rownames
rownames(matrix_count2) <- matrix_count2$ref

## change order of columns
matrix_count2 <- matrix_count2 %>% dplyr::select(-c(oligotype, ref, MED_node_frequency_size, OLIGO_oligotype_frequency_size))

## print it
matrix_count2 %>%
  kbl() %>%
  kable_paper("hover", full_width = F)
CTC1 CTC10 CTC11 CTC12 CTC13 CTC14 CTC15 CTC2 CTC3 CTC4 CTC5 CTC6 CTC7 CTC9 NP27 NP30 NP36 S126 S146 S147 S154 S160 S163 S164 S165 S166 S18 S19 S20 S21 S23 S24 S25 S26 S27 S28 S29 S30 S31 S32 S33 S34 S35 S36 S37 S38 S39 S40 S41 S44 S45 S46 S47 S48 S49 S50 S51 S52
oligotype_C (58) | size:124095 / node_N1160 (58) | size:115121 19626 592 5363 287 2687 1000 14770 1700 2520 1075 326 10704 15 2688 11 1 6 1 4 1 1 1 1 6 2 2 1 12104 12 6 33 53 3 8 1755 5 4 1933 4229 2020 2060 225 392 17944 3328 4744 4931 4388 1 36 32 9 6 221 43 71 61 47
oligotype_G (43) | size:11092 / node_N0990 (43) | size:11092 3017 80 963 43 437 150 2088 254 378 165 60 1649 5 399 1 0 1 0 0 0 0 0 0 1 0 4 0 277 0 0 5 1 0 1 27 0 1 55 121 36 66 5 10 390 71 73 110 130 0 1 1 0 1 6 2 3 1 3 
## edit the fasta dataframe
fasta2 <- fasta2 %>% merge(ref_oligo_med2 %>% dplyr::select(-c(seq)), by="oligotype")
rownames(fasta2) <- fasta2$ref
fasta2 <- fasta2 %>% dplyr::select(-c(MED_node_frequency_size, OLIGO_oligotype_frequency_size, oligotype))

## print it
fasta2 %>%
  kbl() %>%
  kable_paper("hover", full_width = F)
ref
oligotype_C (58) | size:124095 / node_N1160 (58) | size:115121 oligotype_C (58) | size:124095 / node_N1160 (58) | size:115121
oligotype_G (43) | size:11092 / node_N0990 (43) | size:11092 oligotype_G (43) | size:11092 / node_N0990 (43) | size:11092

Metadata

metadata <- read.csv(paste0(path_metadata,"/metadata_08_02_2021.csv"), sep=";")
rownames(metadata) <- metadata$Sample

Phyloseq object with oligotypes

# convert matrix_count into matrix and numeric
matrix_count <- matrix_count2 %>% as.matrix()
class(matrix_count) <- "numeric"

# phyloseq elements
OTU = otu_table(as.matrix(matrix_count), taxa_are_rows =TRUE)
TAX = tax_table(as.matrix(fasta2))
SAM = sample_data(metadata)

# phyloseq object
ps <- phyloseq(OTU, TAX, SAM)
ps
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2 taxa and 58 samples ]
## sample_data() Sample Data:       [ 58 samples by 15 sample variables ]
## tax_table()   Taxonomy Table:    [ 2 taxa by 1 taxonomic ranks ]
compute_read_counts(ps)
## [1] 135187
# remove blanks
ps <- subset_samples(ps, Location!="Blank")
ps <- check_ps(ps)
ps
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2 taxa and 56 samples ]
## sample_data() Sample Data:       [ 56 samples by 15 sample variables ]
## tax_table()   Taxonomy Table:    [ 2 taxa by 1 taxonomic ranks ]

Create new metadata with Percent

Load ps with all samples (for final plot)

setwd(path_rdata)
ps.filter <- readRDS("1D_MED_phyloseq_decontam.rds")
ps.filter <- check_ps(ps.filter)

Edit new metadata with Percent_elizabethkingia

guide_italics <- guides(fill = guide_legend(label.theme = element_text(size = 16, face = "italic", colour = "Black", angle = 0)))

# add read depth in sample table of phyloseq object
sample_data(ps.filter)$Read_depth <- sample_sums(ps.filter)

# select Wolbachia
ps.elizabethkingia <- ps.filter %>% subset_taxa(Genus=="Elizabethkingia")

# add read depth of Wolbachia
sample_data(ps.filter)$Read_elizabethkingia <- sample_sums(ps.elizabethkingia)
sample_data(ps.filter) %>% colnames()
##  [1] "Sample"               "Well"                 "Primer1"             
##  [4] "Primer2"              "Location"             "Field"               
##  [7] "Country"              "Organ"                "Species"             
## [10] "Individual"           "Individuals"          "Date"                
## [13] "Run"                  "Control"              "Dna"                 
## [16] "Read_depth"           "Read_elizabethkingia"
sample_data(ps.elizabethkingia) %>% colnames()
##  [1] "Sample"      "Well"        "Primer1"     "Primer2"     "Location"   
##  [6] "Field"       "Country"     "Organ"       "Species"     "Individual" 
## [11] "Individuals" "Date"        "Run"         "Control"     "Dna"        
## [16] "Read_depth"
# add percent of Wolbachia
sample_data(ps.filter)$Percent_elizabethkingia <- sample_data(ps.filter)$Read_elizabethkingia / sample_data(ps.filter)$Read_depth

# round the percent of Wolbachia at 2 decimals
sample_data(ps.filter)$Percent_elizabethkingia <- sample_data(ps.filter)$Percent_elizabethkingia %>% round(2)

# extract metadata table
test <- data.frame(sample_data(ps.filter))

# merge this metadata table with the other
new.metadata <- data.frame(sample_data(ps)) %>% merge(test %>% dplyr::select(c(Sample, Read_depth, Read_elizabethkingia, Percent_elizabethkingia)), by="Sample")
new.metadata <- test[new.metadata$Sample %in% sample_names(ps),]
rownames(new.metadata) <- new.metadata$Sample

# print it
new.metadata %>%
  kbl() %>%
  kable_paper("hover", full_width = F)
Sample Well Primer1 Primer2 Location Field Country Organ Species Individual Individuals Date Run Control Dna Read_depth Read_elizabethkingia Percent_elizabethkingia
CTC1 CTC1 G5 V4-SA707 V3-SA505 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 58 29779 22643 0.76
CTC10 CTC10 D6 V4-SA704 V3-SA506 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 37,9 2609 672 0.26
CTC11 CTC11 E6 V4-SA705 V3-SA506 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 58 13874 6326 0.46
CTC12 CTC12 F6 V4-SA706 V3-SA506 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 40,1 1146 330 0.29
CTC13 CTC13 G6 V4-SA707 V3-SA506 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 58 18035 3124 0.17
CTC14 CTC14 H6 V4-SA708 V3-SA506 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 6,17 1708 1150 0.67
CTC15 CTC15 I6 V4-SA709 V3-SA506 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 58 23180 16858 0.73
CTC2 CTC2 H5 V4-SA708 V3-SA505 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 58 30692 1954 0.06
CTC3 CTC3 I5 V4-SA709 V3-SA505 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 58 39920 2898 0.07
CTC4 CTC4 J5 V4-SA710 V3-SA505 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 1,15 2139 1240 0.58
CTC5 CTC5 K5 V4-SA711 V3-SA505 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 58 15789 386 0.02
CTC6 CTC6 L5 V4-SA712 V3-SA505 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 58 19753 12353 0.63
CTC9 CTC9 C6 V4-SA703 V3-SA506 Wolbachia - Lab France Whole Culex quinquefasciatus N/A 0 09/03/2018 run2 True sample 33,6 4980 3087 0.62
NP14 NP14 K4 V4-SA711 V3-SA504 Guadeloupe Field Guadeloupe Ovary Aedes aegypti 1a 0 N/A run3 True sample 0,437 7973 0 0.00
NP2 NP2 K3 V4-SA711 V3-SA503 Guadeloupe Field Guadeloupe Ovary Culex quinquefasciatus 1c 0 N/A run3 True sample 41,6 648335 0 0.00
NP20 NP20 E5 V4-SA705 V3-SA505 Guadeloupe Field Guadeloupe Ovary Aedes aegypti 3a 0 N/A run3 True sample 0,357 136 0 0.00
NP27 NP27 L5 V4-SA712 V3-SA505 Guadeloupe Field Guadeloupe Whole Culex quinquefasciatus 7c 0 N/A run3 True sample 1,16 1234 12 0.01
NP29 NP29 B6 V4-SA702 V3-SA506 Guadeloupe Field Guadeloupe Whole Culex quinquefasciatus 9c 0 N/A run3 True sample 0,314 203 0 0.00
NP30 NP30 C6 V4-SA703 V3-SA506 Guadeloupe Field Guadeloupe Whole Culex quinquefasciatus 10c 0 N/A run3 True sample 0,666 228 1 0.00
NP34 NP34 G6 V4-SA707 V3-SA506 Guadeloupe Field Guadeloupe Whole Culex quinquefasciatus 14c 0 N/A run3 True sample 0,486 95 0 0.00
NP35 NP35 H6 V4-SA708 V3-SA506 Guadeloupe Field Guadeloupe Whole Aedes aegypti 7a 0 N/A run3 True sample 4,64 196532 0 0.00
NP36 NP36 I6 V4-SA709 V3-SA506 Guadeloupe Field Guadeloupe Whole Aedes aegypti 8a 0 N/A run3 True sample 1,06 249 7 0.03
NP37 NP37 J6 V4-SA710 V3-SA506 Guadeloupe Field Guadeloupe Whole Aedes aegypti 9a 0 N/A run3 True sample 22,7 419340 0 0.00
NP38 NP38 K6 V4-SA711 V3-SA506 Guadeloupe Field Guadeloupe Whole Aedes aegypti 10a 0 N/A run3 True sample 3,88 282479 0 0.00
NP39 NP39 L6 V4-SA712 V3-SA506 Guadeloupe Field Guadeloupe Whole Aedes aegypti 11a 0 N/A run3 True sample 20,2 218684 0 0.00
NP41 NP41 B7 V4-SA702 V3-SA507 Guadeloupe Field Guadeloupe Whole Aedes aegypti 13a 0 N/A run3 True sample 5,32 247152 0 0.00
NP42 NP42 C7 V4-SA703 V3-SA507 Guadeloupe Field Guadeloupe Whole Aedes aegypti 14a 0 N/A run3 True sample 4,65 185157 0 0.00
NP43 NP43 D7 V4-SA704 V3-SA507 Guadeloupe Field Guadeloupe Whole Aedes aegypti 15a 0 N/A run3 True sample 6,89 239335 0 0.00
NP44 NP44 E7 V4-SA705 V3-SA507 Guadeloupe Field Guadeloupe Whole Aedes aegypti 16a 0 N/A run3 True sample 21,7 156879 0 0.00
NP5 NP5 B4 V4-SA702 V3-SA504 Guadeloupe Field Guadeloupe Ovary Culex quinquefasciatus 2c 0 N/A run3 True sample 33,5 736159 0 0.00
NP8 NP8 E4 V4-SA705 V3-SA504 Guadeloupe Field Guadeloupe Ovary Culex quinquefasciatus 3c 0 N/A run3 True sample 46 334799 0 0.00
S100 S100 K7 V4-SA711 V3-SA507 Camping Europe Field France Ovary Culex pipiens GL1 1 30/05/2017 run1 True sample 8,02 52486 0 0.00
S102 S102 A8 V4-SA701 V3-SA508 Camping Europe Field France Ovary Culex pipiens GL2 2 30/05/2017 run1 True sample 0,241 3456 0 0.00
S104 S104 C8 V4-SA703 V3-SA508 Camping Europe Field France Ovary Culex pipiens GL5 5 30/05/2017 run1 True sample 24,1 52403 0 0.00
S105 S105 D8 V4-SA704 V3-SA508 Camping Europe Field France Ovary Culex pipiens GL6 6 30/05/2017 run1 True sample 6,83 55577 0 0.00
S106 S106 E8 V4-SA705 V3-SA508 Camping Europe Field France Ovary Culex pipiens GL7 7 30/05/2017 run1 True sample 51 33053 0 0.00
S107 S107 F8 V4-SA706 V3-SA508 Camping Europe Field France Ovary Culex pipiens GL8 8 30/05/2017 run1 True sample 32,6 52154 0 0.00
S108 S108 G8 V4-SA707 V3-SA508 Camping Europe Field France Ovary Culex pipiens GL9 9 30/05/2017 run1 True sample 32,2 55735 0 0.00
S109 S109 H8 V4-SA708 V3-SA508 Camping Europe Field France Ovary Culex pipiens GL10 10 30/05/2017 run1 True sample 26,5 59023 0 0.00
S110 S110 I8 V4-SA709 V3-SA508 Camping Europe Field France Ovary Culex pipiens GL11 0 30/05/2017 run1 True sample 27,5 57377 0 0.00
S121 S121 H1 V4-SA708 V3-SA501 Bosc Field France Ovary Culex pipiens J26 22 28/06/2017 run2 True sample 12,7 20361 0 0.00
S122 S122 I1 V4-SA709 V3-SA501 Bosc Field France Ovary Culex pipiens J27 23 28/06/2017 run2 True sample 22,7 9803 0 0.00
S123 S123 J1 V4-SA710 V3-SA501 Bosc Field France Ovary Culex pipiens J28 24 28/06/2017 run2 True sample 6,41 20130 0 0.00
S124 S124 K1 V4-SA711 V3-SA501 Bosc Field France Ovary Culex pipiens J29 25 28/06/2017 run2 True sample 33,9 18146 0 0.00
S126 S126 K6 V4-SA711 V3-SA506 Bosc Field France Ovary Culex pipiens J30 26 28/06/2017 run2 True sample 58 15235 1 0.00
S127 S127 B2 V4-SA702 V3-SA502 Bosc Field France Ovary Culex pipiens J31 27 28/06/2017 run2 True sample 12,8 24696 0 0.00
S128 S128 C2 V4-SA703 V3-SA502 Bosc Field France Ovary Culex pipiens J32 28 28/06/2017 run2 True sample 35,1 16305 0 0.00
S146 S146 I3 V4-SA709 V3-SA503 Lavar (labo) Lab France Ovary Culex pipiens MW52 29 29/08/2017 run2 True sample 35,2 25012 4 0.00
S147 S147 J3 V4-SA710 V3-SA503 Lavar (labo) Lab France Ovary Culex pipiens MW53 30 29/08/2017 run2 True sample 27,1 25171 1 0.00
S148 S148 K3 V4-SA711 V3-SA503 Lavar (labo) Lab France Ovary Culex pipiens MW54 31 29/08/2017 run2 True sample 43,2 14164 0 0.00
S150 S150 A4 V4-SA701 V3-SA504 Lavar (labo) Lab France Ovary Culex pipiens MW55 32 29/08/2017 run2 True sample 2,3 15081 0 0.00
S151 S151 B4 V4-SA702 V3-SA504 Lavar (labo) Lab France Ovary Culex pipiens MW56 33 29/08/2017 run2 True sample 38,8 22944 0 0.00
S152 S152 C4 V4-SA703 V3-SA504 Lavar (labo) Lab France Ovary Culex pipiens MW57 34 29/08/2017 run2 True sample 39,8 15082 0 0.00
S153 S153 D4 V4-SA704 V3-SA504 Lavar (labo) Lab France Ovary Culex pipiens MW58 35 29/08/2017 run2 True sample 52 17040 0 0.00
S154 S154 E4 V4-SA705 V3-SA504 Lavar (labo) Lab France Ovary Culex pipiens MW59 36 29/08/2017 run2 True sample 37,7 9626 1 0.00
S160 S160 K4 V4-SA711 V3-SA504 Lavar (labo) Lab France Ovary Culex pipiens MW60 37 29/08/2017 run2 True sample 58 72508 1 0.00
S162 S162 B5 V4-SA702 V3-SA505 Lavar (labo) Lab France Ovary Culex pipiens MW61 38 29/08/2017 run2 True sample 42 25180 0 0.00
S163 S163 L6 V4-SA712 V3-SA506 Lavar (labo) Lab France Ovary Culex pipiens MW62 39 30/08/2017 run2 True sample 51 12333 1 0.00
S164 S164 C5 V4-SA703 V3-SA505 Lavar (labo) Lab France Ovary Culex pipiens MW63 40 30/08/2017 run2 True sample 36,6 22368 7 0.00
S165 S165 D5 V4-SA704 V3-SA505 Lavar (labo) Lab France Ovary Culex pipiens MW64 41 30/08/2017 run2 True sample 53 17731 2 0.00
S166 S166 E5 V4-SA705 V3-SA505 Camping Europe Field France Ovary Culex pipiens GL4 4 30/05/2017 run2 True sample 23,1 13979 6 0.00
S167 S167 F5 V4-SA706 V3-SA505 Bosc Field France Ovary Culex pipiens J32 28 28/06/2017 run2 True sample 29,1 14048 0 0.00
S169 S169 B7 V4-SA702 V3-SA507 Camping Europe Field France Ovary Culex pipiens 5 43 16/05/2017 run2 True sample 5,84 11553 0 0.00
S170 S170 C7 V4-SA703 V3-SA507 Camping Europe Field France Ovary Culex pipiens 6 44 16/05/2017 run2 True sample 5,55 8852 0 0.00
S18 S18 A1 V4-SA701 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW75 0 30/08/2017 run1 True sample 0,089 4290 1 0.00
S19 S19 B1 V4-SA702 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW65 0 30/08/2017 run1 True sample 21,9 44527 12381 0.28
S20 S20 C1 V4-SA703 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW66 0 30/08/2017 run1 True sample 16,6 42864 12 0.00
S21 S21 D1 V4-SA704 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW67 0 30/08/2017 run1 True sample 12,4 33798 6 0.00
S22 S22 E1 V4-SA705 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW68 0 30/08/2017 run1 True sample 24,1 19044 0 0.00
S23 S23 F1 V4-SA706 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW69 0 30/08/2017 run1 True sample 20,8 38172 38 0.00
S24 S24 G1 V4-SA707 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW70 0 30/08/2017 run1 True sample 34,2 42355 54 0.00
S25 S25 H1 V4-SA708 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW71 0 30/08/2017 run1 True sample 21,9 47688 3 0.00
S26 S26 I1 V4-SA709 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW72 0 30/08/2017 run1 True sample 0,322 5394 9 0.00
S27 S27 J1 V4-SA710 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW73 0 30/08/2017 run1 True sample 11,3 24558 1782 0.07
S28 S28 A2 V4-SA701 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW74 0 30/08/2017 run1 True sample 0,112 4503 5 0.00
S30 S30 K1 V4-SA711 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW1 0 23/08/2017 run1 True sample 4,43 25353 1988 0.08
S31 S31 L1 V4-SA712 V3-SA501 Lavar (labo) Lab France Whole Culex pipiens MW2 0 23/08/2017 run1 True sample 2,66 20417 4350 0.21
S32 S32 C2 V4-SA703 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW3 0 23/08/2017 run1 True sample 0,504 12441 2056 0.17
S33 S33 D2 V4-SA704 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW4 0 23/08/2017 run1 True sample 0,782 33867 2126 0.06
S34 S34 E2 V4-SA705 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW5 0 23/08/2017 run1 True sample 1,38 9367 230 0.02
S35 S35 F2 V4-SA706 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW6 0 23/08/2017 run1 True sample 0,56 11663 402 0.03
S36 S36 G2 V4-SA707 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW7 0 23/08/2017 run1 True sample 39,8 33020 18334 0.56
S37 S37 H2 V4-SA708 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW8 0 23/08/2017 run1 True sample 41,3 18340 3399 0.19
S38 S38 I2 V4-SA709 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW9 0 23/08/2017 run1 True sample 32,1 54790 4817 0.09
S39 S39 J2 V4-SA710 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW10 0 23/08/2017 run1 True sample 37,3 36273 5041 0.14
S40 S40 K2 V4-SA711 V3-SA502 Lavar (labo) Lab France Whole Culex pipiens MW11 0 23/08/2017 run1 True sample 4,58 44448 4518 0.10
S42 S42 A3 V4-SA701 V3-SA503 Camping Europe Field France Whole Culex pipiens GLE1 0 30/05/2017 run1 True sample 0 4107 0 0.00
S43 S43 B3 V4-SA702 V3-SA503 Camping Europe Field France Whole Culex pipiens GLE2 0 30/05/2017 run1 True sample 0,191 9279 0 0.00
S44 S44 C3 V4-SA703 V3-SA503 Camping Europe Field France Whole Culex pipiens GLE3 0 30/05/2017 run1 True sample 0,102 8026 37 0.00
S45 S45 D3 V4-SA704 V3-SA503 Camping Europe Field France Whole Culex pipiens GLE4 0 30/05/2017 run1 True sample 0,223 18150 33 0.00
S47 S47 F3 V4-SA706 V3-SA503 Camping Europe Field France Whole Culex pipiens GLE6 0 30/05/2017 run1 True sample 0,291 1951 7 0.00
S48 S48 G3 V4-SA707 V3-SA503 Camping Europe Field France Whole Culex pipiens GLE7 0 30/05/2017 run1 True sample 3,44 56738 227 0.00
S49 S49 H3 V4-SA708 V3-SA503 Bosc Field France Whole Culex pipiens E1 0 28/06/2017 run1 True sample 1,1 33498 45 0.00
S50 S50 I3 V4-SA709 V3-SA503 Bosc Field France Whole Culex pipiens E2 0 28/06/2017 run1 True sample 0,771 28481 74 0.00
S51 S51 J3 V4-SA710 V3-SA503 Bosc Field France Whole Culex pipiens E3 0 28/06/2017 run1 True sample 17,8 61788 62 0.00
S52 S52 K3 V4-SA711 V3-SA503 Bosc Field France Whole Culex pipiens E4 0 28/06/2017 run1 True sample 0,495 21553 50 0.00
S55 S55 B4 V4-SA702 V3-SA504 Bosc Field France Whole Culex pipiens E6 0 28/06/2017 run1 True sample 2,85 50447 0 0.00
S56 S56 C4 V4-SA703 V3-SA504 Bosc Field France Whole Culex pipiens E7 0 28/06/2017 run1 True sample 3,6 42609 0 0.00
S57 S57 D4 V4-SA704 V3-SA504 Bosc Field France Whole Culex pipiens E8 0 28/06/2017 run1 True sample 4,92 49157 0 0.00
S58 S58 E4 V4-SA705 V3-SA504 Bosc Field France Whole Culex pipiens E9 0 28/06/2017 run1 True sample 1,63 30357 0 0.00
S59 S59 F4 V4-SA706 V3-SA504 Bosc Field France Whole Culex pipiens E10 0 28/06/2017 run1 True sample 1,64 32798 0 0.00
S60 S60 G4 V4-SA707 V3-SA504 Bosc Field France Whole Culex pipiens E11 0 28/06/2017 run1 True sample 2,7 44485 0 0.00
S61 S61 H4 V4-SA708 V3-SA504 Bosc Field France Whole Culex pipiens E12 0 28/06/2017 run1 True sample 2 49545 0 0.00
S63 S63 J4 V4-SA710 V3-SA504 Bosc Field France Whole Culex pipiens E14 0 28/06/2017 run1 True sample 6,13 53444 0 0.00
S64 S64 K4 V4-SA711 V3-SA504 Bosc Field France Whole Culex pipiens E15 0 28/06/2017 run1 True sample 4,15 47628 0 0.00
S79 S79 B6 V4-SA702 V3-SA506 Camping Europe Field France Ovary Culex pipiens J16 12 28/06/2017 run1 True sample 33,8 59755 0 0.00
S80 S80 C6 V4-SA703 V3-SA506 Camping Europe Field France Ovary Culex pipiens J17 13 28/06/2017 run1 True sample 4,58 52788 0 0.00
S83 S83 F6 V4-SA706 V3-SA506 Camping Europe Field France Ovary Culex pipiens J20 16 28/06/2017 run1 True sample 35,5 42272 0 0.00
S84 S84 G6 V4-SA707 V3-SA506 Camping Europe Field France Ovary Culex pipiens J21 17 28/06/2017 run1 True sample 21 56676 0 0.00
S85 S85 H6 V4-SA708 V3-SA506 Camping Europe Field France Ovary Culex pipiens J22 18 28/06/2017 run1 True sample 11,6 41690 0 0.00
S86 S86 I6 V4-SA709 V3-SA506 Camping Europe Field France Ovary Culex pipiens J23 19 28/06/2017 run1 True sample 4,14 61984 0 0.00
S87 S87 J6 V4-SA710 V3-SA506 Bosc Field France Ovary Culex pipiens J24 20 28/06/2017 run1 True sample 28,1 65958 0 0.00
S88 S88 K6 V4-SA711 V3-SA506 Bosc Field France Ovary Culex pipiens J25 21 28/06/2017 run1 True sample 8,6 53102 0 0.00 
# replace metadata in the created phyloseq object
sample_data(ps) <- sample_data(new.metadata)

Taxonomic structure

Count

col <- brewer.pal(7, "Pastel2")

# reshape data for plot
test3 <- test %>% select(c(Sample, Species, Location, Organ, Read_depth, Read_elizabethkingia)) %>% reshape2::melt(id.vars=c("Sample", "Species", "Location", "Organ"), vars=c("Read_depth", "Read_elizabethkingia"))

count_whole <- test3[test3$Organ=="Whole",]
count_ovary <- test3[test3$Organ=="Ovary",]

make.italic <- function(x) as.expression(lapply(x, function(y) bquote(italic(.(y)))))

levels(count_whole$Species)= c("Aedes aegypti"=make.italic("Aedes aegypti"),
               "Culex pipiens"=make.italic("Culex pipiens"),
               "Culex quinquefasciatus"=make.italic("Culex quinquefasciatus"))

levels(count_ovary$Species)= c("Aedes aegypti"=make.italic("Aedes aegypti"),
               "Culex pipiens"=make.italic("Culex pipiens"),
               "Culex quinquefasciatus"=make.italic("Culex quinquefasciatus"))

levels(count_whole$Location) <- c("Bosc", "Camping~Europe", "Guadeloupe", "Lavar~(lab)", expression(paste(italic("Wolbachia"), "- (Slab TC)")))

levels(count_ovary$Location) <- c("Bosc", "Camping~Europe", "Guadeloupe", "Lavar~(lab)", expression(paste(italic("Wolbachia"), "- (Slab TC)")))


# plot
p_count1 <- ggplot(count_whole, aes(x = Sample, y = value, fill=variable))+ 
  geom_bar(position = "dodge", stat = "identity")+
  scale_fill_manual(values = col)+
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, size=12, hjust=1, vjust=0.5)) +
  ggtitle("") + 
  guide_italics+
  theme(legend.title = element_text(size = 20), 
        legend.position="bottom",
        legend.text=element_text(size=14), 
        panel.spacing.y=unit(1, "lines"), 
        panel.spacing.x=unit(0.8, "lines"),
        panel.spacing=unit(0,"lines"),
        strip.background=element_rect(color="grey30", fill="grey90"),
        strip.text.x = element_text(size = 16),
        panel.border=element_rect(color="grey90"),
        axis.ticks.x=element_blank(),
        axis.text.y = element_text(size=18)) +
  facet_wrap(~Species+Location+Organ, scales = "free_x", ncol=3, labeller=label_parsed)+
  labs(y="Sequence counts")+
  ylim(0, 900000)+
  geom_text(aes(label=value), position=position_dodge(width=1.1), width=0.25, size=4, hjust=-0.25, vjust=0.5, angle=90)+
  guides(fill=guide_legend(title="Read"))
## Warning: Ignoring unknown parameters: width
p_count2 <- ggplot(count_ovary, aes(x = Sample, y = value, fill=variable))+ 
  geom_bar(position = "dodge", stat = "identity")+
    scale_fill_manual(values = col)+
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, size=18, hjust=1, vjust=0.5)) +
  ggtitle("") + 
  guide_italics+
  theme(legend.title = element_text(size = 20), 
        legend.position="bottom",
        legend.text=element_text(size=14), 
        panel.spacing.y=unit(1, "lines"), 
        panel.spacing.x=unit(0.8, "lines"),
        panel.spacing=unit(0,"lines"),
        strip.background=element_rect(color="grey30", fill="grey90"),
        strip.text.x = element_text(size = 16),
        panel.border=element_rect(color="grey90"),
        axis.ticks.x=element_blank(),
        axis.text.y = element_text(size=18)) +
 facet_wrap(~Species+Location+Organ, scales = "free_x", ncol=3, labeller=label_parsed)+
  labs(y="Sequence counts")+
    ylim(0, 900000)+
  geom_text(aes(label=value), position=position_dodge(width=0.8), width=0.25, size=4, hjust=-0.25, vjust=0.5, angle=90)+
  guides(fill=guide_legend(title="Read"))
## Warning: Ignoring unknown parameters: width
# afficher plot
p_count1
## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

p_count2

# panels
p_group <- plot_grid(p_count1+theme(legend.position="none"), 
          p_count2+theme(legend.position="none"), 
          nrow=2, 
          ncol=1)+
    draw_plot_label(c("B1", "B2"), c(0, 0), c(1, 0.5), size = 20)
## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals
legend_plot <- get_legend(p_count1 + theme(legend.position="bottom"))
## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals

## Warning: position_dodge requires non-overlapping x intervals
p_counts <- plot_grid(p_group, legend_plot, nrow=2, ncol=1, rel_heights = c(1, .1))
p_counts

Whole (the most abundant nodes)

guide_italics <- guides(fill = guide_legend(label.theme = element_text(size = 16, face = "italic", colour = "Black", angle = 0),
                                            nrow=2, byrow=TRUE))

# select whole
ps.filter.whole <- subset_samples(ps, Organ=="Whole")
ps.filter.whole <- prune_taxa(taxa_sums(ps.filter.whole) >= 1, ps.filter.whole)
ps.filter.whole <- prune_samples(sample_sums(ps.filter.whole) >= 1, ps.filter.whole)
ps.filter.whole
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2 taxa and 45 samples ]
## sample_data() Sample Data:       [ 45 samples by 18 sample variables ]
## tax_table()   Taxonomy Table:    [ 2 taxa by 1 taxonomic ranks ]
# data pour plot
data_for_plot2 <- taxo_data_fast(ps.filter.whole, method = "abundance")
## Warning in `[<-.factor`(`*tmp*`, ri, value = "Other"): invalid factor level, NA
## generated
paste0("\n15 MOST ABUNDANT GENUS: \n") %>% cat()
## 
## 15 MOST ABUNDANT GENUS:
paste0("\"", levels(data_for_plot2$Name), "\",\n") %>% cat()
## "oligotype_C (58) | size:124095 / N1160 (58) | size:115121.",
##  "oligotype_G (43) | size:11092 / N0990 (43) | size:11092.",
##  "Other.",
new_names <- c("oligotype_C (58) | size:124095 / N1160 (58) | size:115121.",
               "oligotype_G (43) | size:11092 / N0990 (43) | size:11092.")

data_for_plot2$Name <- factor(data_for_plot2$Name, levels = new_names)

col_add <- brewer.pal(8, "Accent")

col <- c("oligotype_C (58) | size:124095 / N1160 (58) | size:115121."="#6AEEF7",
         "oligotype_G (43) | size:11092 / N0990 (43) | size:11092."="#6AC3F7")

levels(data_for_plot2$Species)= c("Aedes aegypti"=make.italic("Aedes aegypti"),
               "Culex pipiens"=make.italic("Culex pipiens"),
               "Culex quinquefasciatus"=make.italic("Culex quinquefasciatus"))

levels(data_for_plot2$Location) <- c("Bosc", "Camping~Europe", "Guadeloupe", "Lavar~(lab)", expression(paste(italic("Wolbachia"), "- (Slab TC)")))

data_for_plot2 <- data_for_plot2 %>% na.omit()

p2 <- ggplot(data_for_plot2, aes(x = Sample, y = Relative_Abundance, fill = Name, species=Species, organ=Organ, location=Location))+ 
  geom_bar(position = "stack", stat = "identity")+
  scale_fill_manual(values = col)+
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, size=18, hjust=1, vjust=0.5)) +
  ggtitle("") + 
  guide_italics+
  theme(legend.title = element_text(size = 20), 
        legend.position="bottom",
        legend.text = element_text(size=14),
        #legend.key.height = unit(1, 'cm'),
        panel.spacing.y=unit(1, "lines"), 
        panel.spacing.x=unit(0.8, "lines"),
        panel.spacing=unit(0,"lines"),
        strip.background=element_rect(color="grey30", fill="grey90"),
        strip.text.x = element_text(size = 16),
        panel.border=element_rect(color="grey90"),
        axis.ticks.x=element_blank(),
        axis.text.y = element_text(size=18)) +
  facet_wrap(~Species+Location+Organ, scales = "free", ncol=3, labeller=label_parsed)+
  labs(x="Sample", y="Relative abundance", fill="Oligotype / MED node")

p2

Ovary (the most abundant nodes)

# select ovary
ps.filter.ovary <- subset_samples(ps, Organ=="Ovary")
ps.filter.ovary <- prune_taxa(taxa_sums(ps.filter.ovary) >= 1, ps.filter.ovary)
ps.filter.ovary <- prune_samples(sample_sums(ps.filter.ovary) >= 1, ps.filter.ovary)
ps.filter.ovary
## phyloseq-class experiment-level object
## otu_table()   OTU Table:         [ 2 taxa and 9 samples ]
## sample_data() Sample Data:       [ 9 samples by 18 sample variables ]
## tax_table()   Taxonomy Table:    [ 2 taxa by 1 taxonomic ranks ]
# data pour plot
data_for_plot3 <- taxo_data_fast(ps.filter.ovary, method = "abundance")
## Warning in `[<-.factor`(`*tmp*`, ri, value = "Other"): invalid factor level, NA
## generated
paste0("\n15 MOST ABUNDANT GENUS: \n") %>% cat()
## 
## 15 MOST ABUNDANT GENUS:
paste0("\"", levels(data_for_plot3$Name), "\",\n") %>% cat()
## "oligotype_C (58) | size:124095 / N1160 (58) | size:115121.",
##  "oligotype_G (43) | size:11092 / N0990 (43) | size:11092.",
##  "Other.",
new_names <- c("oligotype_C (58) | size:124095 / N1160 (58) | size:115121.",
               "oligotype_G (43) | size:11092 / N0990 (43) | size:11092.")

data_for_plot3$Name <- factor(data_for_plot3$Name, levels = new_names)

levels(data_for_plot3$Species)= c("Aedes aegypti"=make.italic("Aedes aegypti"),
               "Culex pipiens"=make.italic("Culex pipiens"),
               "Culex quinquefasciatus"=make.italic("Culex quinquefasciatus"))

levels(data_for_plot3$Location) <- c("Bosc", "Camping~Europe", "Guadeloupe", "Lavar~(lab)", expression(paste(italic("Wolbachia"), "- (Slab TC)")))

data_for_plot3 <- data_for_plot3 %>% na.omit()

p3 <- ggplot(data_for_plot3, aes(x = Sample, y = Relative_Abundance, fill = Name, species=Species, organ=Organ, location=Location))+ 
  geom_bar(position = "stack", stat = "identity")+
  scale_fill_manual(values = col)+
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, size=18, hjust=1, vjust=0.5)) +
  ggtitle("") + 
  guide_italics+
  theme(legend.title = element_text(size = 20), 
        legend.position="bottom",
        legend.text = element_text(size=14),
        #legend.key.height = unit(1, 'cm'),
        panel.spacing.y=unit(1, "lines"), 
        panel.spacing.x=unit(0.8, "lines"),
        panel.spacing=unit(0,"lines"),
        strip.background=element_rect(color="grey30", fill="grey90"),
        strip.text.x = element_text(size = 16),
        panel.border=element_rect(color="grey90"),
        axis.ticks.x=element_blank(),
        axis.text.y = element_text(size=18)) +
  facet_wrap(~Species+Location+Organ, scales = "free", ncol=3, labeller=label_parsed)+
  labs(x="Sample", y="Relative abundance", fill="Oligotype / MED node")

p3

Panels taxonomy of whole / ovary

legend_plot <- get_legend(p2 + theme(legend.position="bottom"))

# panels
p_group <- plot_grid(p2+theme(legend.position="none"), 
          p3+theme(legend.position="none"), 
          nrow=2, 
          ncol=1)+
    draw_plot_label(c("A1", "A2"), c(0, 0), c(1, 0.5), size = 20)

p_taxo <- plot_grid(p_group, legend_plot, nrow=2, rel_heights = c(1, .1))
p_taxo

Save taxonomic plot

setwd(path_plot)

tiff("2Dd_OLIGO_counts_elizabethkingia.tiff", units="in", width=20, height=18, res=300)
p_counts
dev.off()
## quartz_off_screen 
##                 2
tiff("2Dd_OLIGO_taxonomic_elizabethkingia_whole.tiff", units="in", width=16, height=12, res=300)
p2
dev.off()
## quartz_off_screen 
##                 2
tiff("2Dd_OLIGO_taxonomic_elizabethkingia_ovary.tiff", units="in", width=18, height=14, res=300)
p3
dev.off()
## quartz_off_screen 
##                 2
tiff("2Dd_OLIGO_taxonomic_elizabethkingia.tiff", units="in", width=18, height=16, res=300)
p_taxo
dev.off()
## quartz_off_screen 
##                 2
png("2Dd_OLIGO_counts_elizabethkingia_big.png", units="in", width=20, height=18, res=300)
p_counts
dev.off()
## quartz_off_screen 
##                 2
png("2Dd_OLIGO_counts_elizabethkingia_small.png", units="in", width=18, height=14, res=300)
p_counts
dev.off()
## quartz_off_screen 
##                 2
png("2Dd_OLIGO_taxonomic_elizabethkingia_whole.png", units="in", width=16, height=12, res=300)
p2
dev.off()
## quartz_off_screen 
##                 2
png("2Dd_OLIGO_taxonomic_elizabethkingia_ovary.png", units="in", width=18, height=14, res=300)
p3
dev.off()
## quartz_off_screen 
##                 2
png("2Dd_OLIGO_taxonomic_elizabethkingia_big.png", units="in", width=18, height=18, res=300)
p_taxo
dev.off()
## quartz_off_screen 
##                 2
png("2Dd_OLIGO_taxonomic_elizabethkingia_small.png", units="in", width=18, height=14, res=300)
p_taxo
dev.off()
## quartz_off_screen 
##                 2

Make main plot

setwd(paste0(path_oligo,"/elizabethkingia/oligotyping_Elizabethkingia_sequences-c1-s1-a0.0-A0-M10/HTML-OUTPUT"))

img <- magick::image_read("entropy.png")
p_entropy <- magick::image_ggplot(img, interpolate = TRUE)
p_entropy+ theme(plot.margin = unit(c(-7,-2.5,-7,-0.5), "cm"))

p_entropy+ theme(plot.margin=unit(c(-7,-2,-12,-5), "mm"))

aligned <- plot_grid(p_taxo, 
                     p_counts, 
                     align="hv")

aligned

p_entropy2 <- plot_grid(p_entropy, nrow=1)+
  draw_plot_label(c("C"), c(0), c(1), size=20, hjust=-0.5)

p_entropy2

t_plot <- plot_grid(aligned, 
                    p_entropy2,
                    nrow=2, 
                    ncol=1, 
                    scale=1,
                    rel_heights=c(2,1))

t_plot

setwd(path_plot)

tiff("2Dd_OLIGO_main_elizabethkingia.tiff", width=36, height=36, res=300, units="in")
t_plot
dev.off()
## quartz_off_screen 
##                 2
png("2Dd_OLIGO_main_elizabethkingia.png", width=36, height=36, res=300, units="in")
t_plot
dev.off()
## quartz_off_screen 
##                 2